Automated system and method for improving healthcare communication

ABSTRACT

Systems, methods and tools for improving healthcare communication between physicians and patients by utilizing audio recordings systems capable of collecting voice data of patient conversations with healthcare providers. The communication system converts the recorded voice data into text using voice to text conversion software, analyzes the voice data using a natural language processor to parse for key words and phrases relating to the patient&#39;s health and concerns. Voice data may be additionally analyzed by cognitive analysis systems and machine learning algorithms designed to identify the sentiment that the patient is portraying while discussing the patient&#39;s concerns about health-related experiences or symptoms and cross-referenced with social media and other external websites or applications, confirming a patient&#39;s sentiment or providing additional key words and phrases unraised by the patient when communicating with the physician.

BACKGROUND

The present invention relates to data collection, data analytics, andmore specifically to embodiments of automated healthcare communicationsystems.

Healthcare patients often have trouble communicating healthcare concernswith doctors diagnosing and treating the patients. The lack of effectivecommunication is partially due to limited face time doctors have witheach patient and the high number of patients that a doctor communicateswith on any given day. Often, Information that is given to a doctor'sassistant may be lost or misinterpreted by the assistant.

SUMMARY

A first embodiment of the present disclosure provides a method forautomating healthcare communication comprising the steps of recording,by a processor of a computing system, voice data of a patient;converting, by the processor, the voice data to text; parsing, by theprocessor, the text of the voice data for key words; analyzing, by theprocessor, the voice data for sentiment and stress variables indicatinga heightened stress of the patient; ranking, by the processor, the keywords as a function of the sentiment and stress variables analyzed; andgenerating, by the processor, a list corresponding to the ranking of thekey words.

A second embodiment of the present disclosure provides a computer systemcomprising a processor; a memory device coupled to the processor; anaudio recording system; and a computer readable storage device coupledto the processor, wherein the storage device contains program codeexecutable by the processor via the memory device to implement a methodfor automating healthcare communication comprising the steps of:receiving, by the processor of a computing system, voice data of apatient recorded by the audio recording device; converting, by theprocessor, the voice data to text; parsing, by the processor, the textof the voice data for key words; analyzing, by the processor, the voicedata for sentiment and stress variables indicating a heightened stressof the patient; ranking, by the processor, the key words as a functionof the sentiment and stress variables analyzed; and generating, by theprocessor, a list corresponding to the ranking of the key words.

A third embodiment of the present disclosure provides a computer programproduct comprising a computer readable hardware storage device storing acomputer readable program code, the computer readable program codecomprising an algorithm that when executed by a computer processor of acomputing system implements a method for automating healthcarecommunication, the method comprising the steps of: receiving, by aprocessor of a computing system, voice data of a patient recorded by avoice recording system; converting, by the processor, the voice data totext; parsing, by the processor, the text of the voice data for keywords; analyzing, by the processor, the voice data for sentiment andstress variables indicating a heightened stress of the patient; ranking,by the processor, the key words as a function of the sentiment andstress variables analyzed; and generating, by the processor, a listcorresponding to the ranking of the key words.

A fourth embodiment of the present disclosure provides a method forautomating healthcare communication comprising the steps of: receiving,by a processor of a computing system, voice data of a patient recordedby an audio recording system; converting, by the processor, the voicedata to text; parsing, by the processor, the text of the voice data forkey words; further receiving, by the processor, video data of thepatient recorded by a video recording system; tagging, by the processor,the video data of the patient with one or more tagged key words;analyzing, by the processor, voice data and video data for sentiment andstress variables indicating a heightened stress of the patient; ranking,by the processor, the key words and tagged key words as a function ofthe sentiment and stress variables identified during the analyzing step;generating, by the processor, a list corresponding to the ranking of thekey words; and displaying, by the processor, the list generated by theprocessor on a graphical user interface.

A fifth embodiment of the present disclosure provides a computer systemcomprising: a processor; a memory device coupled to the processor; anaudio recording system; a video recording system; and a computerreadable storage device coupled to the processor, wherein the storagedevice contains program code executable by the processor via the memorydevice to implement a method for automating healthcare communicationcomprising the steps of: receiving, by a processor of a computingsystem, voice data of a patient recorded by the audio recording system;converting, by the processor, the voice data to text; parsing, by theprocessor, the text of the voice data for key words; further receiving,by the processor, video data of the patient recorded by the videorecording system; tagging, by the processor, the video data of thepatient with one or more tagged key words; analyzing, by the processor,voice data and video data for sentiment and stress variables indicatinga heightened stress of the patient; ranking, by the processor, the keywords and tagged key words as a function of the sentiment and stressvariables identified during the analyzing step; generating, by theprocessor, a list corresponding to the ranking of the key words; anddisplaying, by the processor, the list generated by the processor on agraphical user interface.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a block diagram of a healthcare communication system, inaccordance with the embodiments of the present invention.

FIG. 2 depicts a block diagram of an alternative embodiment of ahealthcare communication system in accordance with the embodiments ofpresent invention.

FIG. 3 depicts a block diagram of a second alternative embodiment of ahealthcare communication system in accordance with the embodiments ofthe present invention.

FIG. 4 depicts a schematic view of an embodiment of a healthcarecommunication system installed in an embodiment of an officeenvironment.

FIG. 5 depicts an embodiment of a computer system displaying a graphicaluser interface of a healthcare communication system.

FIG. 6a depicts an embodiment of a computer system displaying agraphical user interface of a healthcare communication system generatingan embodiment of a patient report.

FIG. 6b depicts an embodiment of a computer system displaying agraphical user interface of a healthcare system generating analternative embodiment of a patient report.

FIG. 7 depicts a cloud computing environment of a healthcarecommunication system, in accordance with embodiments of the presentinvention.

FIG. 8 depicts abstraction model layers of a cloud computing environmentof a healthcare communication system, in accordance with embodiments ofthe present invention

FIG. 9 depicts a flow chart of an embodiment of a method for automatinghealthcare communication.

FIG. 10 depicts a flow chart of an alternative embodiment of a methodfor automating healthcare communication.

FIG. 11 depicts a block diagram of a computer system for the healthcarecommunication system of FIG. 1, capable of implementing methods forautomating healthcare communication of FIGS. 9-10, in accordance withembodiments of the present invention.

DETAILED DESCRIPTION

Although certain embodiments are shown and described in detail, itshould be understood that various changes and modifications may be madewithout departing from the scope of the appended claims. The scope ofthe present disclosure will in no way be limited to the number ofconstituting components, the materials thereof, the shapes thereof, therelative arrangement thereof, etc., and are disclosed simply as anexample of embodiments of the present disclosure. A more completeunderstanding of the present embodiments and advantages thereof may beacquired by referring to the following description taken in conjunctionwith the accompanying drawings, in which like reference numbers indicatelike features.

As a preface to the detailed description, it should be noted that, asused in this specification and the appended claims, the singular forms“a”, “an” and “the” include plural referents, unless the context clearlydictates otherwise.

Overview

Existing systems for facilitating communication between doctor andpatient consist of verbal and electronic communications sent directlybetween patient and doctor or direct communications between the patientand employees of the doctor, such as nurses, receptionist or physicianassistants. Patients often have trouble effectively communicating healthconcerns to the doctor directly or patients have trouble emphasizing theimportance of the patients' concerns, which may render the doctor asappearing uncaring or not listening as carefully as the physician shouldbe. The shortcomings of direct communication may be a result of limitedface time that the physician may have with patients or the high volumeof patients that the doctor may interact with during any given work day.Simultaneously, information that may be provided to assistants, nursesand employees of the doctor's office or hospital may be forgotten,overlooked or misinterpreted by the recipient and thus result in thedoctor never receiving or not fully grasping the importance of theinformation provided by the patient. Moreover, in some instances,patients may simply not emphasize concerns during face to face time withthe doctor. The lack of information or misinterpreted information beingcommunicated about the patient's healthcare may cause the doctors tofail in the identification of informational trends in symptoms orcomplaints by the patient, ultimately leading to the receipt of inferiorhealthcare.

Embodiments of the present disclosure improve healthcare communicationbetween doctors, the doctor's employees and the patients, minimizing theeffects of underemphasized, lost or misinterpreted details that may havebeen important for doctors to focus on. Embodiments of the presentdisclosure provide alternative opportunities for patients' concerns anddiscussions to be readily analyzed and sent to the doctor even when thedoctor is not in the presence of the patient. Embodiments of the presentdisclosure leverage computer systems engaging in data collection, dataanalytics, natural language processing, machine learning, cognitiveanalysis of verbal communications and/or visual cues of body language,to identify, track and emphasize symptoms and concerns of patients thatmay be important for a healthcare professional to notice.

Embodiments of the healthcare communication systems described herein mayutilize audio recordings systems to collect voice data of the patientfrom numerous discussions that may occur in the presence or absence ofthe physician, while the patient is within range of the audio recordingdevices. The audio recording system may include one or more microphonesor other types of audio recording devices positioned throughout thehealthcare provider's facilities. The microphones and recording devicesmay capture communications between the patient and the medical office'spersonnel. The healthcare communication system may convert the recordedvoice data into text using voice to text conversion software. Theresulting text created from the voice data may be analyzed using anatural language processor, parsed for key words and phrases relating tothe patient's health and concerns. The key words and phrases identifiedmay be tabulated to account for the frequency in which the identifiedkey words and phrases are mentioned in the collected voice data, as wellas tracking the key words over a period of time in order to determinewhether the concerns are reoccurring and/or becoming a bigger concern ofthe patient.

In some embodiments of the healthcare communication system, voice datamay be additionally analyzed by cognitive analysis systems and machinelearning algorithms designed to identify the sentiment that the patientis portraying while discussing the patient's concerns abouthealth-related experiences or symptoms. The healthcare communicationsystem may be programmed to detect variables in the human voice that maybe indicators of stress or anxiety (stress variables). For example, thesystem may detect anger, frustration, sadness, fear etc. in thepatient's recorded voice data, indicating a heightened level of stressbeing experienced by the patient as the patient is describinghealth-related experiences. The stress variables may potentially provideclues to the physician about topics of concern that may requireadditional focus or emphasis while treating the patient in the future.

Embodiments of the healthcare communication system may rank the keywords in order of perceived importance as a function of the frequency inwhich the key words appear in the voice data and as a function of thestress levels identified by the natural language processor. Key wordsand topics may be provided to the physician in the form of a report thatmay be viewed by physician. The report may help to focus the physician'sattention and provide a written structure for the physician to followand address the concerns of the patient. Upon viewing the reportcomprising the ranked key words and topics, the physician may becomeaware of potential health concerns that the physician may not haveconsidered as being important for the particular patient. The keywords,concerns and stress levels of the patient may evolve over time. Thereport generated may be continuously updated over time as the patient'sconcerns and keywords change, allowing for the healthcare communicationsystem to track a patient's healthcare concerns

Embodiments of the healthcare communication system described herein mayfurther amend the rankings of key words and phrases generated in thereport provided to the physician with additional data from an externaldata source. For example, a social media website, application, programor messaging service. The healthcare communication system may scanexternal data sources for additional information about the patient andsupplement the collected voice data to provide additional context of thepatient's concerns. For instance, a patient may post numerous times on asocial media website about a particular symptom or healthcare concern.The healthcare communication system may parse the patient's posts andconsider the content of the posts while analyzing key words of the voicedata collected by the audio recording system. Embodiments of thehealthcare communication system may factor the data collected from theexternal data sources when ranking the key words and phrases parsed fromthe collected audio data in order to gauge the overall importance of aparticular topic.

Healthcare Communication System

Referring to the drawings, FIGS. 1-8 illustrate embodiments of ahealthcare communication system 100, 200, 300, 400 for automatinghealthcare communication between patients and physicians, consistentwith the disclosures of this application. Embodiments of system 100 maycomprise a specialized computer system, referred to as communicationsystem 101. Embodiments of the communication system 101 operating as aspecialized computer system may include a processor 115, specializedhardware or circuitry, chipsets and/or software loaded in the memorydevice 116 of the communication system 101. Embodiments of thecommunication system 101 may perform one or more functions, tasks orroutines relating to the recordation of voice data, converting the voicedata to text, parsing the voice data for key words and phrases,accessing contextual data from one or more network accessible datasources 131 a, 131 b . . . 131 n (hereinafter referred to collectivelyas “data sources 131”), tagging the collected and recorded data with oneor more tags, ranking the key words and phrases as a function ofimportance and generating reports presenting the ranked key words andphrases that may be identified over a specific period of time.

Embodiments of the specialized hardware and/or software integrated intothe communication system 101 may be integrated into the communicationsystems 101 as part of a communication module 103. The communicationmodule 103 may include hardware components and software applicationsperforming each of the functions of the communication system 101,including, but not limited to the recordation of voice data, convertingthe voice data to text, parsing the voice data, accessing contextualdata from data sources 131, tagging the voice data, ranking the keywords and phrases and generating reports. As used herein, the term“module” may refer to a hardware module, software-based module or amodule may be a combination of hardware and software resources of thecommunication system 101 and/or resources remotely accessible to thecommunication system 101 via a network 130.

The hardware and/or software components of the communication system 101may include one or more sub modules performing one or more specifictasks of the communication system. The sub modules actively performingthe tasks of a particular embodiment of the communication system 101 mayvary. Examples of sub modules that may be part of the communicationmodule 103 may include a recording module 105, voice to text module 107,parsing module 108, data collection module 109, tagging module 111,analytics module 113 and/or reporting module 114.

Embodiments of system 100, may include a plurality of computer systemsconnected and placed in communication with one another over a computernetwork 130, including one or more network accessible computing devices133 a, 133 b . . . 133 n (hereinafter referred to collectively as“network accessible computer devices 133”), network accessiblerepositories 140, voice recording systems 125 or video recording systems123. Embodiments of the network 130 may be constructed using wired orwireless connections between each hardware component connected to thenetwork 130. As shown in the embodiments of FIGS. 2-3, each of thecomputer systems 101, 123, 125, 133, 140 may connect to the network 130and communicate over the network 130, for example using a networkinterface controller (NIC) 119 or other network communication device.Embodiments of the NIC 119 may implement specialized electroniccircuitry allowing for communication using a specific physical layer anda data link layer standard, such as Ethernet, Fiber channel, Wi-Fi orToken Ring. The NIC 119 may further allow for a full network protocolstack, enabling communication over network 130 to the group of computersystems 101, 123, 125, 133, 140 or other computing hardware deviceslinked together through communication channels.

The network 130 may facilitate communication and resource sharing amongthe computer systems 101, 123, 125, 133, 140 and additional hardwaredevices connected to the network 130. Embodiments of the presentdisclosure are capable of being implemented in conjunction with any typeof computing environment now known or later developed. For instance, thenetwork 130 may include a local area network (LAN), home area network(HAN), wide area network (WAN), back bone networks (BBN), peer to peernetworks (P2P), cloud computing environment 150, campus networks,enterprise networks, the Internet, and any other network known by aperson skilled in the art.

Referring to the drawings, FIG. 1 illustrates a diagram of a healthcarecommunication system 100 for automating healthcare communication betweenpatient and physician. Embodiments of system 100 may comprisecommunication system 101. Embodiments of the communication system 101may be a computing system comprising hardware and software components asdepicted in embodiment 100 and in some embodiments, may integrate one ormore components of a generic computer system 1100 described in detailbelow. Embodiments of the communication system 101 may comprise aprocessor 115, memory device 116, an input/output (I/O) interface 117and one or more computer readable data storage devices 120 as understoodby individuals skilled in the art and in accordance with processors,memory devices, I/O interfaces and computer readable storage devicesthat may be part of a generic computing system 1100. Embodiments of thecommunication system 101 may include a communication network module 103comprising specific hardware modules and/or software modules loaded intothe memory device 116.

In some embodiments of the communication system 101, the communicationmodule 103 may include a recording module 105. The recording module 105may perform the function or task of collecting and retrieving voice datafrom a voice recording system 125 and/or video data from a videorecording system 123 connected to the communication system 101. As shownby the various embodiments of the figures, the voice recording system125 and video recording system 123 may be connected locally to thecommunication system via an I/O interface 117 as depicted in embodiments100, 200. In alternative embodiments, the voice 125 and video recordingsystems 123 may be connected to the communication system 101 overnetwork 130. In some embodiments of the healthcare communication system100, 200, 300, 400 the voice recording system 125 may include one ormore microphones or digital voice recording devices, whereas the videorecording system 123 may include one or more camera system. In someembodiments, the voice recording system 125 and the video recordingsystem 123 may be combined into a single unit comprising a camera andmicrophone which may record and transmit both voice and video to therecording module 105.

In some embodiments of the healthcare communication system 100, 200,300, 400, the video recording system 123 and voice recording system 125may continuously stream video data and voice data to the recordingmodule 105 of the communication module 103. The recording module 105receiving the voice data and video data may organize and store the voiceand video data in one or more local storage devices 120 or networkaccessible data repositories 140, for further processing and analysis bythe communication system 101. In alternative embodiments, the recordingmodule may not always be recording voice and video data, but may insteadselectively record voice and video data when vocal sounds initiate thevoice recording system 125 and/or movement initiates the video recordingsystem 123. In some embodiments of the communication system, therecording module 105 may include wired or wireless connectivityhardware, such as Wi-Fi, Bluetooth, Bluetooth low energy, infrared,Zigbee, WiMAX or other communication technologies capable of sending andreceiving signals between the recording module 105 and the voicerecording system and/or video recording system.

Embodiments of the communication module 103 may further comprise a voiceto text module. Embodiments of the voice to text module 107 may performthe function of converting voice data received from the voice recordingsystem 125 to computer readable format comprising text. In someembodiments, the voice to text module 107 may convert the voice datacollected by the recording module 105 into text by translating an analogwave recorded by the microphone of the voice recording system 125 intodigital data via an analog-to-digital converter (ADC). The voice to textmodule 107 and/or the microphone of the voice recording system 125 maydigitize the speech of the patient and or healthcare professionalsconversing, by taking measurements of the analog waves produced byspeech at a series of intervals. The voice to text module 107 may filterthe digitized sound of the recorded voice data to remove unwantedbackground noise or to separate the sounds into different bands offrequency. The voice to text module 107 may match segments of therecorded voice data to known phenomes of the language recorded by thevoice recording system 125. Voice to text module 107 may match phenomeswith the context of the other phenomes recorded to create a contextualphenome plot. The contextual phenome plot may be processed through astatistical model comparing the sounds of each phenome in the recordedvoice data to a library of known words, phrases and sentences stored bythe communication system 101 or a network accessible repository 140.Once the words of the voice data have been identified, the voice datamay be outputted as text and stored as a file by the voice to textmodule 107 for further analysis and processing by the additional submodules of the communication module 103.

Embodiments of the communication module 103 may include a parsing module108. The parsing module 108 may perform the task or function ofanalyzing the text output of the voice to text module 107 for one ormore healthcare related key words and phrases. The parsing module mayprocess the text output of the voice to text module 107 by transformingthe text into a parse tree or an abstract syntax tree in someembodiments. The parsing of the text, performed by the parsing module108, may be performed by three different components. The components mayinclude a scanner, lexer and parser. The scanner may read the text beingparsed one character at a time. As the scanner reads each character ofthe text, the scanner may pass the character information to the lexer,which may transform the stream of characters into a stream of tokens.During tokenization, the string of input characters may be classifiedand passed on to the parser. The parser reads the stream of tokensproduced by the lexer and builds a parse tree or syntax tree inaccordance with a set of language rules. As the parsing module buildsthe parse tree or syntax tree, one or more key words or phrases may beidentified as important or contextually appropriate to healthcare.Embodiments of the parsing module 108 may track the keywords or phrasesrelating to healthcare which repeatedly appear in the parsed text aswell as track the number of times each of the keywords appear.

In some embodiments, the parsing module 108 may further comprise anatural language processor which may responsible for performing the taskof providing a sentiment analysis of the text output received from thevoice to text module 107. Sentiment Analysis is the process ofdetermining whether a piece of writing (in this case the outputted text)is positive, negative or neutral. Sentiment analysis may also derive theopinion or attitude of a speaker. The natural language processor may beresponsible for systematically identifying the attitude and emotionalreaction of the speaker recorded in the voice data, which may providecontext to the emotions of a patient conveying health concerns orsymptoms. Sentiment analysis may be performed using one or moredifferent methods to characterize the text outputted by the voice tottext module 107. For example, the natural language processor of theparsing module 108 may store a predetermined list of positive andnegative words, which may be weighted depending on each words' strengthin conveying positive or negative qualities.

In alternative embodiments, sentiment analysis may be performed by thenatural language processor using machine learning. Machine learningtechniques may rely on the communication system's 101 ability toautomatically learn from the language used, expressions of sentiment.Machine learning may be used by first providing training to the naturallanguage processor. For example, by providing sample sets of positiveand negative language which may denote particular sentiments andemotions. Numerous examples sets may be provided to the natural languageprocessor during the training phase. The more examples provided, themore accurately the natural language processor may accurately predictthe sentiment and emotions conveyed in the text output of the recordedvoice data. Once the natural language processor has learned from theexamples provided in the training sequence, the natural languageprocessor can apply the acquired knowledge to new and unseen textoutputs from the voice tot text module 107 and classify the text intoone or more different sentiments.

Moreover, in some embodiments, the natural language processor mayadditionally be trained to identify various sentiments that may indicatestress in the voice of the recorded individual (i.e. the patient). Forexample, certain sentiment may be classified as more indicative ofstress, such as anger, anxiousness and fright, whereas other sentimentssuch as happiness and joy may not be indicators of stress.

In some embodiments of the communication systems 101 the accuracy ofidentifying the stress level and sentiment of the patient from recordedvoice data may be improved by further recording and analyzing the bodylanguage of the patient while speaking. For example, in someembodiments, the communication system 101 may be placed into electroniccommunication with a video recording system 123. The video recordingsystem may capture digital video of the patient communicating withhealthcare personnel at the same time that voice data is recorded by thevoice recording system 125. Video data recorded by the video recordingsystem may be streamed and transmitted to the recording module 105. Thevideo data may be processed by a video analysis module capable ofdetecting body language through the presentation of hand gestures,specific movements and facial expressions captured in the video data.Similar to the natural language processor, video analysis module mayundergo machine learning techniques to properly train the video analysismodule about the positive and negative connotations of specific bodylanguage.

In some embodiments, the voice data and video data may be encoded with atime stamp. The communication module 103, and more particularly theanalytics module (described below) may utilize the encoded time stamp tocompare the sentiment analysis of the voice data and the body languageof the patient at the time the voice data was provided. The inclusion ofboth audio and visual analysis of the patient may improve the accuracyof the conclusions drawn about the patient's sentiment when certain keywords or phrases regarding healthcare and symptoms are spoken.

Embodiments of the communication system 101 may further cross referencekeywords extrapolated from voice data and the sentiment analysisthereof, with other network accessible data sources 131 that may provideadditional context and insight into the patient's healthcare concerns.The data collection module 109 may perform the function of retrievingadditional data from external data sources available to thecommunication system over network 130. Network accessible data sources131 may include, but are not limited to data retrieved from social mediaprofiles, emails, direct messaging services, websites, SMS textmessages, recorded phone conversations, etc. For example, the datacollection module 109 may retrieve a series of social media posts of apatient's social media profile over a period of time complaining aboutone or more medical symptoms. Moreover, the data collection module 109may further scan network accessible data sources 131 for additionalchanges in a patient's lifestyle that may indicate a medical concern orcomplaint that a patient may have, indicating that a patient may besuffering from a condition and/or compensating for a condition that thepatient may be unaware of. As the communication system 101 compilesareas of concern to present to a physician, the additional dataretrieved from network accessible data sources may be used as a crossreference with the keywords parsed from the voice data and/or treated asa separate set of patient information that may be cause for concern bythe physician.

In some embodiments of the communication module 103, the communicationmodule may be equipped with a tagging module 111. The tagging module 111analyze one or more types of data collected by the communication systemand input one or metadata tags in the collected data file in order tocategorize and classify the collected data. For example, the taggingmodule may apply one or more tags in the metadata of the voice data,video data, text outputted from the voice to text module 107, thekeywords and phrases outputted by the parsing module 108, and thenetwork accessible data sources 131 retrieved by the data collectionmodule 109. The tags applied to the each piece of data generated orcollected by the system 100, 200, 300, 400 may describe the data andmake it easier for the system 100, 200, 300, 400 to search and query thecollected data by tag. Moreover, in some embodiments, the tagging module111 may further apply date tags to each of the pieces of data collectedby the system 100, 200, 300, 400. By applying dates as tags to each ofthe different types of collected data, the communication system 101 maygenerate queries between specified date ranges in order to gauge thesentiment and concerns of the patient during different moments in time.

Embodiments of the communication system 101 may comprise an analyticsmodule 113. the data analytics module 113 perform qualitative andquantitative techniques and processes on the data created by the voicerecording systems 125, video recording systems 123 and networkaccessible data sources 131. Using each piece of collected dataharvested by the communication system 101, the analytics module 113 mayidentify patterns to patient behavior and draw conclusions. Inparticular, the analytic module 113 may identify patterns relating tothe patient's healthcare concerns or symptoms.

Embodiments of the analytics module 113 may be responsible for drawingconclusions about each patient's healthcare concerns and ranking theconcerns of the patient as a function of keywords' sentiment of thevoice data collected, the stress levels associated with the patient'ssentiment while communicating the identified keywords and the frequencyof the keywords being identified in the parsed voice data. The analyticsmodule may further organize and rank keywords and phrases associatedwith the voice data as a function of cross referencing the keywords withnetwork accessible data sources comprising comments and indicators ofconcern about particular keywords, separate from the voice data as wellas video analysis of the patient's body language collected by the videorecording system 123.

For example, the analytics module 113 may rank keywords or phrasesassociated with medical symptoms as a higher point of concern for thephysician to further focus on when the same or similar keywords appearfrequently or repeatedly. Frequently occurring keywords may be rankedhigher than keywords that appear less frequently or infrequently.Likewise, keywords or phrases associated with high levels of stress andanxiety may be rated higher keywords that are identified as having alower level of stress or anxiety in the patient when the keywords arediscussed. In some embodiments, the analytics module 113 may rateparticular keywords higher in instances where there is repeateddiscussion by the patient either to non-physicians or socially (i.e. onsocial media). The analytics system 113 may conclude that the keywordsor symptoms not being raised to the physician should be and thus theanalytics system may rank undiscussed keywords that may not have beencollected as voice data during patient-physician discussions highly inorder to focus the physician on addressing the matter directly with thepatient.

Embodiments of the communication module 103 may further comprise areporting module 114. The reporting module 114 may perform the task ofgenerating and/or displaying a list of ranked key words and/or symptomsorganized by the analytics module 113 as a function of the voice data,video data and/or network accessible data sources 131. The reportingmodule 114 may present the ranked list of keywords in a computerreadable format and may display the report comprising the ranked list ofkeywords on a graphical user interface 521 of a display device 121.

The reports generated by the reporting module 114 may, in someembodiments be transmitted by the reporting module 114 to a separatecomputing device operated by a physician or other healthcare personnel.For example, instead of displaying the report about the keywordsassociated with the patient on a display system 121 attached to thecommunication system 101, the report may be transmitted to one or morenetwork accessible computer devices 133 a, 133 b . . . 133 n. Thenetwork accessible computer devices 133 may be any type of computersystem. For example, the network accessible computer systems 133 may bea desktop computer, laptop, tablet, cell phone, smart phone or portablecomputing device. By transmitting the reports to physicians and otherhealthcare professions over the network, the physician and employeesthereof do not need to be particularly tethered to the computer system101 generating the report on each patient's keywords and concerns.Delivery to mobile devices may allow for the physician to become mobileand view different patient reports while treating the patients in thephysician's office. The reports may be stored, saved and cataloged to acomputer readable storage device such as storage device 120 or networkrepository 140, allowing for the physician to access multiple reportsgenerated by the reporting module and organize each report by date. Thismay allow for the physician to review old keywords and symptoms andcompare the report to the newest assessment of a particular patient,thus gauging the progress of the patient's concerns, whether theconcerns are being addressed and to determine if any unaddressedkeywords remain that may require the focus of the physician.

Referring to the drawings, FIG. 4 represents an embodiment of ahealthcare communication system 400 which may be installed in aphysician's office, hospital or other healthcare treatment facility. Asshown in the figure, the communication system 101 and display system121, may be placed in a separate location from the voice recordingsystem 125 and the video recording system 123. In the example shown, thecommunication system 101 is placed in the physician's room 405 whereasthe plurality of voice recording devices 125 a-125 d and the videorecording devices 123 a-123 d are placed throughout the office as shown.In the particular system 400 shown in FIG. 4, the voice recordingdevices 125 a-125 d and the video recording devices 123 a-123 d areplaced in the waiting room 401 and examination rooms 403 a-403 c. Byhaving multiple recording devices positioned throughout the office orfacility being observed, the patient of interest may be monitoredseamlessly as the patient moves and converses with the physician orhealthcare providers in the office.

Referring to the drawings, FIG. 5 depicts an embodiment of acommunication system 101 displaying graphical user interface 521provided by the communication system 101. The graphical user interface521 may be accessible by the physician or healthcare personnel that maybe responsible for treating the patient being observed by thecommunication system. Records of each patient may include patientinformation 501 and one or more notations 503 providing a summary of thepatient's concerns both identified by the communication system 101 viavoice data as well as from external data obtain from one or more networkaccessible data sources 131. Embodiments of the graphical user interface(GUI) 521 may allow for a user of GUI 521 to access and query reports505 generated by the reporting module 114.

As shown in FIGS. 6a and 6b , the GUI 521 may display one or moredifferent reports 505 comprising one or more ranked lists of keywordsderived from the data collected by the communication system 101. Forexample, in FIG. 6a , a report generated within a particular time frame(year 2016) shows an example of keywords ranked by frequency. Thekeywords include symptoms such as headache, nausea, dizziness, nosebleedand fatigue, however any keywords may be identified by the communicationsystem 101 as a function of the voice data and data sources 131. As itcan be seen by comparing FIG. 6a with 6 b, the rankings may changedepending on the ranking criteria. As opposed to the ranking of keywordsby frequency in FIG. 6 a, FIG. 6b depicts an example of a report 505ranking the identified keywords based on the stress level of thepatient. Whereas headaches, nausea and fatigue were the most frequentlyidentified keywords in FIG. 6a , the most stressful keywords asidentified by the report of the patient in FIG. 6b were the occurrenceof nosebleeds and dizziness. By using multiple ranking systems, aphysician may be able to determine which keywords are most important tothe patient. In this current example, a physician may have been toldabout the dizziness and nosebleeds less frequently and thus dismissed orforgot about such symptoms due to the infrequent discussion. Thephysician, upon viewing the sentiment and stress related to thedizziness and nosebleeds experience by the patient, may choose to focusa discussion with the patient on the identified symptoms causing thepatient the most stress.

FIG. 7 depicts an alternative embodiment of the healthcare communicationsystem 700 wherein the system is operating under a cloud computingmodel. Cloud computing is a model of service delivery enablingconvenient, on-demand network access to a shared pool of configurablecomputing resources (e.g., networks, network bandwidth, servers,processing, memory, storage, applications, virtual machines, andservices) that can be rapidly provisioned and released with minimalmanagement effort or interaction with a provider of the service. Thiscloud model may include at least five characteristics, at least threeservice models, and at least four deployment models. The characteristicsof the cloud computing model may be described as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

The service models under a cloud computing environment may be describedas follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices 101, 133a-133 c through a thin client interface such as a web browser (e.g.,web-based e-mail). The consumer does not manage or control theunderlying cloud infrastructure including network, servers, operatingsystems, storage, or even individual application capabilities, with thepossible exception of limited user-specific application configurationsettings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

The deployment models of cloud computing environments may be describedas follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment may be service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes 110.

Referring to the drawings, FIG. 7 is illustrative of a network 130operating as a cloud computing environment 150. As shown, the cloudcomputing environment may include one or more cloud computing nodes 110with which client computing devices 101, 133 a-133 c used by cloudconsumers, such as, for example, desktop computers, laptop computer,mobile devices, tablet computers or cellular telephones may communicate.Computer system nodes 110 may communicate with one another and may begrouped (not shown) physically or virtually, in one or more networks,such as Private, Community, Public, or Hybrid clouds as describedhereinabove, or a combination thereof, allowing for the cloud computingenvironment of the medical network 150 to offer infrastructure,platforms and/or software as services for which a cloud consumer doesnot need to maintain resources on a local computing device. It isunderstood that the types of computing devices shown in FIG. 7 areintended to be illustrative only and that nodes 110 and cloud computingenvironment can communicate with any type of computerized device overany type of network and/or network addressable connection (e.g., using aweb browser).

Referring now to FIG. 8, a set of functional abstraction layers providedby a cloud computing environment of the cloud network 150 is shown. Itshould be understood in advance that the components, layers, andfunctions shown in FIG. 8 are intended to be illustrative only andembodiments of the invention are not limited thereto. As depicted, thefollowing layers and corresponding functions are provided:

Hardware and software layer 160 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 161;RISC (Reduced Instruction Set Computer) architecture based servers 162;servers 163; blade servers 164; storage devices 165; and networkingcomponents 166. In some embodiments, software components may includenetwork application server software 167 and database software 168.

Virtualization layer 170 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers171; virtual storage 172; virtual networks 173, including virtualprivate networks; virtual applications and operating systems 174; andvirtual clients 175.

Embodiments of the management layer 180 may provide the functionsdescribed below. Resource provisioning 181 provides dynamic procurementof computing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 182provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 183 provides access to the cloud computing environment ofthe medical network 150 for consumers (i.e. prospective and existingpatients) and system administrators. Service level management 184provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 185 provides pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 190 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: recordsmanagement 191; web page management 192; searching and resultsmanagement 193; data analytics processing 194; profile management 195;and healthcare communication 103.

Method for Automating Healthcare Communication

The drawing of FIG. 9-10 represents embodiments 900, 1000 of analgorithm that may be implemented for automating healthcarecommunication, in accordance with the systems described in FIGS. 1-8using one or more computer systems defined generically in FIG. 11 below,and more specifically by the specific embodiments depicted in FIGS. 1-8.A person skilled in the art should recognize that the steps of themethod described in FIGS. 9-10 may not require all of the stepsdisclosed herein to be performed, nor does the algorithm of FIGS. 9-10necessarily require that all the steps be performed in the particularorder presented. Variations of the method steps presented in FIGS. 9-10may be performed in a different order than presented by FIGS. 9-10.

The algorithm 900, described in FIG. 9, may initiate at step 901. Instep 901, the recording module 105 of the communication system 101 mayreceive and record audio of patient conversations as voice data. Thevoice data being recorded may be inputted into one or more recordingdevices of a voice recording system 125 connected to the communicationsystem 101. The voice data being received by the recording module 105,formatted and stored as a voice recording in the storage device 120and/or a network accessible storage device such as network repository140.

In step 903 of method 900, the audio recorded by the voice recordingsystem in step 901 may be converted from audio to text. The voice totext module 107 may receive the voice data and apply one or more speechto text algorithms. Once converted to text, the parsing module 108 mayparse the text obtained from the voice to text module 107 in order toidentify a series of one or more key words used in audio conversationsby the patient. The parsing module may, in some embodiments, identifythe frequency of keywords being repeated by the patient.

In step 905 of the algorithm 900, the parsing module 108 may be equippedwith a natural language processor. The natural language processor mayanalyze the keywords and phrases parsed from the outputted text of step903 for sentiment and stress levels associated with the key words,phrase and thus the recorded voice data's audio. The natural languageprocessor may identify stress variables associated with the patient'skey words and identify the patient's mood or attitude at the time thevoice data was recorded in step 901. In step 909, the algorithm mayfurther compare the sentiment and stress level of the patient withpreviously collected data in order to establish one or more trends inthe sentiment of the patient when one or more particular keywords arespoken. If, in step 909 there is previous data collected by thecommunication system 101, the algorithm may proceed to step 911, whereinthe previously collected data is analyzed for trends in increasingstress levels between the key words most recently identified in step 905and previous keywords, otherwise, the algorithm may proceed to step 913.

In step 913, the algorithm 900 may collect additional data from externalsources that may not be maintained by communication system 101, such asnetwork accessible data sources 131. The data collection module 109 mayscan one or more data sources 131, including social media websites,applications and messaging services for additional data that may beparsed into keywords or provide context for the patient's symptoms orhealthcare concerns. In step 915, the data collection module scanningone or more data sources 131 may identify one or more lifestyle changesdescribed by the patient. The lifestyle change or other describedmatters in the data sources 131 may include information that may nothave been conveyed to the physician therefore may influence the rankingof one or more keywords that may be presented to the physician forfurther discussion between the patient and physician.

In step 917, each of the keywords associated with symptoms, healthcareor medical stressors may be ranked as a function of the patient'ssentiment, stress level, frequency and frequency of the keywords beingused by the patient when the voice data was recorded by the voicerecording system 125 and may be further ranked within the context ofadditional data collected in step 913. Each of the ranked keywords may,in step 919 be presented to the physician as part of a report generatedby the reporting module 114.

Similar to the embodiment of the algorithm 900, the algorithm 1000presents an alternative method for automating healthcare communication.Similar to algorithm 900, algorithm 1000 performs steps 901-905. In step906 however, the algorithm 1000 further introduces a step comprisingrecording video data of the patient. For example using a video recordingsystem 123 connected to the communication system 101. The inclusion ofvideo data may further allow for the communication system 101 to assessthe patient's body language during the recordation of the voice datacollected in step 901. In step 908, the parsing module 108 and thenatural language processor may perform the sentiment analysis and stresslevel identification of the voice data as previously described in step907. However in step 908, the communication system 101 may furtheridentify sentiment and stress level in light of a biometric analysis ofthe body language of the patient displayed by the recorded video data atthe time of the recordation of the voice data.

In step 909, the algorithm 1000 may proceed similar to algorithm 900 asdescribed above. However, if in step 909 it is determined that previousdata has been collected by communication system, the algorithm 1000 mayproceed to step 912 wherein the currently collected data is not onlyanalyzed for trends directed toward increased stress levels associatedwith the parsed key words of step 905 and 908, but also in view of thebody language identified by the communication system during thebiometric analysis of the patient while espousing the voice datacollected in step 901. Subsequently, the algorithm 1000 may be completedfrom steps 913 to steps 919 in a manner similar to the algorithm 900described above.

Computer System

Referring to the drawings, FIG. 11 illustrates a block diagram of acomputer system 1100 that may be included in the systems of FIG. 1—andfor implementing the methods for automating healthcare communication asdescribed in the algorithms of FIGS. 9-10 and in accordance with theembodiments described in the present disclosure. The computer system1100 may generally comprise a processor 1191, otherwise referred to as acentral processing unit (CPU), an input device 1192 coupled to theprocessor 1191, an output device 1193 coupled to the processor 1191, andmemory devices 1194 and 1195 each coupled to the processor 1191. Theinput device 1192, output device 1193 and memory devices 1194, 1195 mayeach be coupled to the processor 1191 via a bus 1190. Processor 1191 mayperform computations and control the functions of computer 1100,including executing instructions included in the computer code 1197 fortools and programs for automating healthcare communication, in themanner prescribed by the embodiments of the disclosure using the systemsof FIGS. 1-8 wherein the instructions of the computer code 1197 may beexecuted by processor 1191 via memory device 1195. The computer code1197 may include software or program instructions that may implement oneor more algorithms for implementing the methods for automatinghealthcare communication, as described in detail above and in FIGS.9-10. The processor 1191 executes the computer code 1197. Processor 1191may include a single processing unit, or may be distributed across oneor more processing units in one or more locations (e.g., on a client andserver).

The memory device 1194 may include input data 1196. The input data 1196includes any inputs required by the computer code 1197, 1198. The outputdevice 1193 displays output from the computer code 1197, 1198. Either orboth memory devices 1194 and 1195 may be used as a computer usablestorage medium (or program storage device) having a computer readableprogram embodied therein and/or having other data stored therein,wherein the computer readable program comprises the computer code 1197,1198. Generally, a computer program product (or, alternatively, anarticle of manufacture) of the computer system 1100 may comprise saidcomputer usable storage medium (or said program storage device).

Memory devices 1194, 1195 include any known computer readable storagemedium, including those described in detail below. In one embodiment,cache memory elements of memory devices 1194, 1195 may provide temporarystorage of at least some program code (e.g., computer code 1197, 1198)in order to reduce the number of times code must be retrieved from bulkstorage while instructions of the computer code 1197, 1198 are executed.Moreover, similar to processor 1191, memory devices 1194, 1195 mayreside at a single physical location, including one or more types ofdata storage, or be distributed across a plurality of physical systemsin various forms. Further, memory devices 1194, 1195 can include datadistributed across, for example, a local area network (LAN) or a widearea network (WAN). Further, memory devices 1194, 1195 may include anoperating system (not shown) and may include other systems not shown inthe figures.

In some embodiments, rather than being stored and accessed from a harddrive, optical disc or other writeable, rewriteable, or removablehardware memory device 1194, 1195, stored computer program code 1198(e.g., including algorithms) may be stored on a static, non-removable,read-only storage medium such as a Read-Only Memory (ROM) device 1199,or may be accessed by processor 1191 directly from such a static,non-removable, read-only medium 1199. Similarly, in some embodiments,stored computer program code 1197 may be stored as computer-readablefirmware 1199, or may be accessed by processor 1191 directly from suchfirmware 1199, rather than from a more dynamic or removable hardwaredata-storage device 1195, such as a hard drive or optical disc.

In some embodiments, the computer system 1100 may further be coupled toan input/output (I/O) interface (for example I/O interface 117) and acomputer data storage unit (for example a data store, data mart orrepository). An I/O interface may include any system for exchanginginformation to or from an input device 1192 or output device 1193. Theinput device 1192 may be, inter alia, a keyboard, joystick, trackball,touchpad, scanning device, bar code reader, mouse, sensors, beacons,RFID tags, microphones, recording device, biometric input device,camera, timer, etc. The output device 1193 may be, inter alia, aprinter, a plotter, a display device (such as a computer screen ormonitor), a magnetic tape, a removable hard disk, a floppy disk, etc.The memory devices 1194 and 1195 may be, inter alia, a hard disk, afloppy disk, a magnetic tape, an optical storage such as a compact disc(CD) or a digital video disc (DVD), a dynamic random access memory(DRAM), a read-only memory (ROM), etc. The bus 1190 may provide acommunication link between each of the components in computer 1100, andmay include any type of transmission link, including electrical,optical, wireless, etc.

The I/O interface may allow computer system 1100 to store information(e.g., data or program instructions such as program code 1197, 1198) onand retrieve the information from a computer data storage unit (notshown). Computer data storage units include any known computer-readablestorage medium, which is described below. In one embodiment, computerdata storage unit may be a non-volatile data storage device, such as amagnetic disk drive (i.e., hard disk drive) or an optical disc drive(e.g., a CD-ROM drive which receives a CD-ROM disk).

As will be appreciated by one skilled in the art, in a first embodiment,the present invention may be a method; in a second embodiment, thepresent invention may be a system; and in a third embodiment, thepresent invention may be a computer program product. Any of thecomponents of the embodiments of the present invention can be deployed,managed, serviced, etc. by a service provider able to deploy orintegrate computing infrastructure with respect to automating healthcarecommunication. Thus, an embodiment of the present invention discloses aprocess for supporting computer infrastructure, where the processincludes providing at least one support service for at least one ofintegrating, hosting, maintaining and deploying computer-readable code(e.g., program code 1197, 1198) in a computer system (e.g., computer1100) including one or more processor(s) 1191, wherein the processor(s)carry out instructions contained in the computer code 1197 causing thecomputer system to automate healthcare communication. Another embodimentdiscloses a process for supporting computer infrastructure, where theprocess includes integrating computer-readable program code into acomputer system including a processor.

The step of integrating includes storing the program code in acomputer-readable storage device of the computer system through use ofthe processor. The program code, upon being executed by the processor,implements a method for automating healthcare communication as describedin this application. Thus the present invention discloses a process forsupporting, deploying and/or integrating computer infrastructure,integrating, hosting, maintaining, and deploying computer-readable codeinto the computer system 1100, wherein the code in combination with thecomputer system 1100 is capable of performing a method for automatinghealthcare communication between a patient and healthcare provider.

A computer program product of the present invention comprises one ormore computer readable hardware storage devices having computer readableprogram code stored therein, said program code containing instructionsexecutable by one or more processors of a computer system to implementthe methods of the present invention.

A computer program product of the present invention comprises one ormore computer readable hardware storage devices having computer readableprogram code stored therein, said program code containing instructionsexecutable by one or more processors of a computer system to implementthe methods of the present invention.

A computer system of the present invention comprises one or moreprocessors, one or more memories, and one or more computer readablehardware storage devices, said one or more hardware storage devicescontaining program code executable by the one or more processors via theone or more memories to implement the methods of the present invention.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer system comprising a processor; amemory device coupled to the processor; a digital audio recordingsystem; a video recording system having a camera system; and a computerreadable storage device coupled to the processor, wherein the storagedevice contains program code executable by the processor via the memorydevice to implement a method for automating healthcare communicationcomprising the steps of: detecting, by said processor via said digitalaudio recording system, audible sounds from a plurality of audiorecording devices positioned throughout facilities of a physician;enabling, by said processor in response to said detecting said audiblesounds, recording functionality of said digital audio recording system;receiving, by the processor in response to said detecting and saidenabling, voice data of a patient communicating with various personnelof said facilities recorded by the digital audio recording system from acontinuous audio stream; detecting, by said processor, human voicevariables within said voice data; converting, by the processor, thevoice data to text, wherein said converting comprises: translating ananalog wave recorded by a microphone of the digital audio recordingsystem into digital data via execution of an analog-to-digital converterby digitizing speech of the voice data via determined measurements ofthe analog wave with respect to a series of intervals; removing unwantedbackground noise from said digital data; and separating sounds of saiddigital data into different bands of frequency; parsing, by theprocessor, the text of the voice data for key words, wherein saidparsing comprises: reading, via a specialized scanner component of saidcomputing system, the text being parsed one character, of characters, ata time; transforming, by a specialized lexer component of said computingsystem, a stream of characters into a stream of tokens; reading, by aspecialized parser component of said computing system, the stream oftokens; building a parse tree based on the stream of tokens and inaccordance with language rule code; and identifying, one or moreassociated key words or phrases associated with a specified frequency ofusage; analyzing, by the processor based on said human voice variables,the voice data for sentiment and stress variables with respect to saidspecified frequency of usage thereby indicating a heightened stress ofthe patient; further analyzing, by the processor, video data of thepatient recorded by a video recording system for additional evidence ofthe sentiment and stress variables further corresponding to theheightened sense of stress of the patient identified in the voice data,wherein said further analyzing comprises: enabling, by said processor inresponse to said detecting movement of said patient, recordingfunctionality of said video recording system comprising a plurality ofvideo recording devices positioned throughout said facilities of saidphysician; detecting, by said processor via said video recording system,visual cues of body language of said patient communicating with saidvarious personnel of said facilities; and identifying, tracking, andemphasizing, by said processor based on said visual cues, symptoms andconcerns of said patient with respect to an importance to saidphysician; ranking, by the processor, the key words as a function of thesentiment and stress variables analyzed with respect to the video dataand the voice data; generating, by the processor, a list correspondingto the ranking of the key words; and enabling, by said processor, saidphysician to review said list thereby enabling said physician toinitiate communication with said patient for resolving the heightenedsense of stress of the patient with respect to medical symptoms.
 2. Thecomputer system of claim 1, wherein the method of automating healthcarecommunication further comprises the steps of: retrieving, by theprocessor, patient data from one or more network accessible datasources; and further analyzing, by the processor, the sentiment andstress variables indicating the heightened sense of stress of thepatient as a function of the patient data retrieved from the one or morenetwork accessible data sources.
 3. The computer system of claim 2,wherein the one or more network accessible data sources is a socialmedia website.
 4. The computer system of claim 1, wherein the listgenerated by the processor comprises said one or more keywords organizedby frequency of the key word within a specified time frame or as afunction of a stress level of the patient providing the voice datacomprising the key words.
 5. A computer program product, comprising acomputer readable hardware storage device storing a computer readableprogram code, the computer readable program code comprising an algorithmthat when executed by a computer processor of a computing systemimplements a method for automating healthcare communication, the methodcomprising the steps of: detecting, by said processor via said digitalaudio recording system, audible sounds from a plurality of audiorecording devices positioned throughout facilities of a physician;enabling, by said processor in response to said detecting said audiblesounds, recording functionality of said digital audio recording system;receiving, by said processor in response to said detecting and saidenabling, voice data of a patient communicating with various personnelof said facilities recorded by said digital voice recording system froma continuous audio stream; detecting, by said processor, human voicevariables within said voice data; converting, by the processor, thevoice data to text, wherein said converting comprises: translating ananalog wave recorded by a microphone of the digital audio recordingsystem into digital data via execution of an analog-to-digital converterby digitizing speech of the voice data via determined measurements ofthe analog wave with respect to a series of intervals; and removingunwanted background noise from said digital data; and separating soundsof said digital data into different bands of frequency; parsing, by theprocessor, the text of the voice data for key words, wherein saidparsing comprises: reading, via a specialized scanner component of saidcomputing system, the text being parsed one character, of characters, ata time; transforming, by a specialized lexer component of said computingsystem, a stream of characters into a stream of tokens; reading, by aspecialized parser component of said computing system, the stream oftokens; building a parse tree based on the stream of tokens and inaccordance with language rule code; and identifying, one or moreassociated key words or phrases associated with a specified frequency ofusage; analyzing, by the processor based on said human voice variables,the voice data for sentiment and stress variables with respect to saidspecified frequency of usage thereby indicating a heightened stress ofthe patient; further analyzing, by the processor, video data of thepatient recorded by the video recording system for additional evidenceof the sentiment and stress variables further corresponding to theheightened sense of stress of the patient identified in the voice data,wherein said further analyzing comprises: enabling, by said processor inresponse to said detecting movement of said patient, recordingfunctionality of said video recording system comprising a plurality ofvideo recording devices positioned throughout said facilities of saidphysician; detecting, by said processor via said video recording system,visual cues of body language of said patient communicating with saidvarious personnel of said facilities; and identifying, tracking, andemphasizing, by said processor based on said visual cues, symptoms andconcerns of said patient with respect to an importance to saidphysician; ranking, by the processor, the key words as a function of thesentiment and stress variables analyzed with respect to the video dataand the voice data; generating, by the processor, a list correspondingto the ranking of the key words; and enabling, by said processor, saidphysician to review said list thereby enabling said physician toinitiate communication with said patient for resolving the heightenedsense of stress of the patient with respect to medical symptoms.
 6. Thecomputer program product of claim 5, wherein the algorithm furthercomprises: retrieving, by the processor, patient data from one or morenetwork accessible data sources; and further analyzing, by theprocessor, the sentiment and stress variables indicating the heightenedsense of stress of the patient as a function of the patient dataretrieved from the one or more network accessible data sources.
 7. Thecomputer program product of claim 6, wherein the one or more networkaccessible data sources is a social media website.
 8. The computerprogram product of claim 5, wherein the list generated by the processorcomprises one or more keywords organized by frequency of the key word inthe text of the voice data or as a function of a stress level of thepatient providing the voice data comprising the key words.
 9. Thecomputer program product of claim 5, wherein the algorithm furthercomprises: further receiving, by the processor, video data of thepatient recorded by a video recording system; further analyzing, by theprocessor, the video data for additional evidence of sentiment andstress variables further corresponding to the heightened sense of stressof the patient identified in the voice data.
 10. A computer systemcomprising: a processor; a memory device coupled to the processor; adigital audio recording system; a video recording system having a camerasystem; and a computer readable storage device coupled to the processor,wherein the storage device contains program code executable by theprocessor via the memory device to implement a method for automatinghealthcare communication comprising the steps of: detecting, by saidprocessor via said digital audio recording system, audible sounds from aplurality of audio recording devices positioned throughout facilities ofa physician; enabling, by said processor in response to said detectingsaid audible sounds, recording functionality of said digital audiorecording system; receiving, by the processor in response to saiddetecting, voice data of a patient communicating with various personnelof said facilities recorded by the digital audio recording system from acontinuous audio stream; detecting, by said processor, human voicevariables within said voice data; converting, by the processor, thevoice data to text, wherein said converting comprises: translating ananalog wave recorded by a microphone of the digital audio recordingsystem into digital data via execution of an analog-to-digital converterby digitizing speech of the voice data via determined measurements ofthe analog wave with respect to a series of intervals; and removingunwanted background noise from said digital data; and separating soundsof said digital data into different bands of frequency; parsing, by theprocessor, the text of the voice data for key words, wherein saidparsing comprises: reading, via a specialized scanner component of saidcomputing system, the text being parsed one character, of characters, ata time; transforming, by a specialized lexer component of said computingsystem, a stream of characters into a stream of tokens; reading, by aspecialized parser component of said computing system, the stream oftokens; building a parse tree based on the stream of tokens and inaccordance with language rule code; and identifying, one or moreassociated key words or phrases associated with a specified frequency ofusage; further receiving, by the processor, video data of the patientrecorded by a video recording system, wherein said further receivingcomprises: enabling, by said processor is response to said detectingmovement of said patient, recording functionality of said videorecording system comprising a plurality of video recording devicespositioned throughout said facilities of said physician; detecting, bysaid processor via said video recording system, visual cues of bodylanguage of said patient communicating with said various personnel ofsaid facilities; and identifying, tracking, and emphasizing, by saidprocessor based on said visual cues, symptoms and concerns of saidpatient with respect to an importance to said physician; tagging, by theprocessor, the video data of the patient with one or more tagged keywords; analyzing, by the processor based on said human voice variables,the voice data for sentiment and stress variables with respect to saidspecified frequency of usage thereby indicating a heightened stress ofthe patient; ranking, by the processor, the key words and tagged keywords as a function of the sentiment and stress variables identifiedduring the analyzing step; generating, by the processor, a listcorresponding to the ranking of the key words; displaying, by theprocessor, the list generated by the processor on a graphical userinterface; and enabling, by said processor, said physician to reviewsaid list thereby enabling said physician to initiate communication withsaid patient for resolving the heightened sense of stress of the patientwith respect to medical symptoms.
 11. The computer system of claim 10,further comprising: retrieving, by the processor, patient data from oneor more network accessible data sources; further analyzing, by theprocessor, the sentiment and stress variables indicating the heightenedsense of stress of the patient as a function of the patient dataretrieved from the one or more network accessible data sources.
 12. Thecomputer system of claim 11, wherein the one or more network accessibledata sources is a social media website.
 13. The computer system of claim10, wherein the list generated by the processor comprises the one ormore keywords and one or more tagged key words organized by frequency ofuse within a specified time frame or as a function of a stress level ofthe patient.
 14. The computer system of claim 10, wherein the audiorecording system and the video recording system wirelessly communicatewith the processor via a wireless network connection.